Predicting Violence Against Women Using the Women’s Economic Opportunity Index (WEOI)

An exploration of the the main indicators of the WEOI and what they can tell us about violence against women

Introduction

Even though “achieving gender equality and empowering all women and girls” is among one of the 17 Sustainable Development Goals by the United Nations (“Goal 5 Department of Economic and Social Affairs” n.d.), such goal swims against an enormous wave of violence against women in the contemporary world. Investigation on the causes of such violence is neither exhaustive nor simple. This paper aims to investigate what are possible reasons for the bugging persistence of violence against women, specifically in what regard to a positive attitudes towards violence against women, i.e., more women finding justifiable that a husband would beat their wives.

Believing in the importance of promoting Women’s economic well-being in order to achieve equality, we aim to understand the relationship between violence against women and the Women’s Economic Opportunity Index (WEOI). Providing a violence-free environment for women and girls will immensely support their empowerment, which in turn represents countless gains for society as a whole (King and Mason 2001).

After a brief literature review on the subject of women’s economic well-being, we explain our two-part methodology. We pulled our data from the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), the World Bank (WB), and the Economist Intelligence Unit (EIU). Our analysis reveals that even though women’s legal status seems to be one of the best predictors within the WEOI for attitude towards violence against women, we must be attentive to the ways in which indicators interact within and outside of the Women’s Economic Opportunity Index affect each other.

Literature Review

Women are disadvantaged in entrepreneurship. Part of that is because structures that push for entrepreneurship have for long pretended to be “genderless” when they are not (Pathak, Goltz, and W. Buche 2013). It is well-known that organizations, by not identifying or addressing the barriers that women might uniquely face, such as child-rearing and domestic labour (Acker 1990), have failed to provide equality in the workplace. Our research dialogues with previous literature in asserting that difficulty in women’s access to finance and labor participation further limit the possibility of women gaining independence from, at best, their domestic and child-rearing duties, and at worse, from their partners. By constraining women from participating in labor in equal and fair manners, as well accessing finance, we argue that this could be increasing violence against women.

Women’s education and legal status can both potentially decrease levels of violence against women. When education is intentionally designed to address gender inequality, it has the potential to provide the tools women need to both identify and report experiences of violence (Okenwa-Emgwa and Strauss 2018). Similarly, women’s legal status is especially important because it provides them with access to institutions and legal frameworks they can resort to when experiencing violence (Tavares and Quentin 2018). Not only does “legal status” include explicit regulation against violence experienced by intimate partners, but also legal frameworks protecting their rights to freedom of movement, property ownership, adolescent fertility, and regulations addressing all forms of discrimination (“Women’s Economic Opportunity 2012” n.d.). However, the literature has raised questions on the effectiveness of both education and legal status in raising the stature of women and preventing violence alone. For example, even though the benefits of education alone are evident (“Girl Rising Girls Education Nonprofit” n.d.), in order to effectively reduce violence against women, it is necessary to formulate curricula that include gender equality and information on violence against women (Okenwa-Emgwa and Strauss 2018). In the case of women’s legal status, adequate implementation and observation of such laws are needed for them to be effectively enforced (Tavares and Quentin 2018). Our research interacts with such limitations by exploring whether WEOI’s indicators on education and training and women’s legal status can predict a decrease in positive attitude towards violence.

Methodology

This study consists of two parts. The first part is an exploration of three indicators that the Organisation for Economic Co-operation and Development (OECD) has linked to Violence Against Women (“Inequality - Violence Against Women - OECD Data” n.d.).

The second part of this research uses the Women’s Economic Opportunity Index (WEOI) to predict violence against women. We dissect the index into four out of its five main variables (labour participation, access to finance, women’s legal status, and women’s education and training), and we use these four factors as our independent variables. Our dependent variable is attitudes towards violence against women. We draw a causal graph to predict the factors involved in causing negative attitudes towards violence, and then we then run several univariate and multivariate regression models were between our dependent and independent variables.

We conclude that from our four main indicators, Women’s Legal Status seems to have the biggest potential to predict a decrease in attitude towards violence. From there, we highlight subvariables within women’s legal status that have a relationship with our main dependent variable. Finally, we analyze the causal relationship between the democracy index and the legal status of women, which may be influencing attitudes towards women

Theory/Hypotheses

With the literature in mind, and with a curiosity towards measurements taken by the Women’s Economic Opportunity Index, our research question consists on how do labour participation, women’s legal status, access to finance and women’s education and training appear to reduce or increase violence against women? Specifically, which one of those four variables seem to be the best predictor for an increase or reduction in attitude towards violence?

Even though we explore four out of the five indicators used in WEOI in order to possibly find out which indicators seem to best predict attitude towards violence, we also pay attention to the interactions between such indicators. We do so because we are well aware of possible confounding variables.

Even though we highlight Women’s Legal Status as a significant predictor to Attitude Towards Violence, We argue that it’s not possible to isolate one single indicator that seems to impact attitude towards women the most.

An Overview of the Data

Our dependent variables consists on the three indicators by the OECD. The first indicator is Attitude Towards Violence, which represents the share of women who agree that a husband is justified in beating his wife. The second is Prevalence of Violence in Lifetime (0-100%), which indicates the share of women exposed to physical and/or sexual violence from an intimate partner at least once in their lives. Finally, Laws on Domestic Violence (0.25-1) measures whether legal frameworks adequately protect women from domestic violence. It is measured on a scale from 0 to 1, where 1 indicates that the laws are completely discriminatory against women.

Our independent variables are the main indicators in the Women’s Economic Opportunity Index (WEOI). Created by the Economist Intelligence Unit, the research division of the Economist Group, this index “is a dynamic quantitative and qualitative scoring model, constructed from 29 indicators, that measures specific attributes of the environment for women employees and entrepreneurs in 128 economies” (“Women’s Economic Opportunity 2012” n.d.). The index is measured on a scale from 0 - 100 where 0 refers to the least favorable situation for women and 100 refers to the most favorable situation to women.

We have decided to drop the fifth main indicator “General Business Environment.” Whereas this could be a good indicator for observing practical obstacles for the women in the workplace, we thought that focusing on the first four indicators could make our research more consistent.

Labour participation consists in four indicators: equal pay for equal work, non-discrimination, degree of de facto discrimination against women in the workplace, and availability, affordability, and, quality of childcare services (“Women’s Economic Opportunity 2012” n.d.)

Access to finance consists in four indicators: building credit histories, women’s access to finance programmes, delivering financial services, private-sector credit as a percent of Gross Domestic Product (“Women’s Economic Opportunity 2012” n.d.).

Education and training consists of four indicators: School life expectancy (primary and secondary), school life expectancy (tertiary), adult literacy rate, and existence of government or non-government programmes offering small and medium-sized enterprise (SME) support/development training (“Women’s Economic Opportunity 2012” n.d.).

Women’s legal status consists of five indicators: existence of laws protecting women against violence, freedom of movement, property ownership rights, adolescent fertility rate, and whether a country ratification of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) (“Women’s Economic Opportunity 2012” n.d.).

Data Analysis

Part 1: Exploration of Dependent and Independent Variables

Visualizing the Dependent Variables: The OECD Violence Against Women Indicators

In our first stage of data exploration, we determine whether there is an association between a country’s economy and its citizens’ attitudes towards violence against women. Intuitively, we expected wealthier and more developed economies to have more economic opportunities for women, leading to more favourable attitudes towards women.

There seems to be a clear association between attitude towards violence against women and a country’s income group. High and upper middle income countries seem to have a lower share of women who agree that the husband is justified in inflicting violence on their wives.

Similarly to attitudes towards violence, when it comes to prevalence of violence in the lifetime, we can observe that the share of women who have experienced violence in high-income countries (colored red) is concentrated towards the left side of the graph. This means that fewer women in these countries have been exposed to physical or sexual violence in comparison to countries falling in other income groups.

Compared with the other indicators, laws on domestic violence does not appear to have a clear association with income group. In the graph, we can see that most countries, regardless of income group, fall in the “0.75” measure for Laws on Domestic Violence, suggesting that their laws and practices are insufficcient to guarantee the well-being of women. Even though there are very few countries with a value of 1 unit (laws and practices are completely discriminatory against women), it is incredibly worrying that most countries in the globe still have insufficcient laws to protect women’s rights. We also observe that there is a higher proportion of countries with favourable laws towards women (indicator value at level 0.25) among high income and upper middle income countries. For both lower middle income and low income, however, there seems to be more countries in the 0.5 and 0.75 levels than in the 0.25 level.

This unveils a pattern where higher income countries, despite having a large proportion of countries with an indicator level equal to 0.75, also have a more countries with somewhat beneficial laws and practices against domestic violence. However, this does not seem to be a causal effect. It is highly unlikely that a country’s income group alone establishes the laws on domestic violence, but it points us in the direction of establishing a connection between higher levels of economic development and better legislation on domestic violence.

We proceed to plot stacked bars that can help us better understand patterns in the data, specifically when it comes to laws on domestic violence. However, one important aspect to note is that the total number of countries in each income category will affect the visualization of these stack bars when they are not designed to take into account proportion:

# A tibble: 4 × 2
  IncomeGroup         count
  <fct>               <int>
1 High income            39
2 Upper middle income    33
3 Lower middle income    37
4 Low income             19

There are 39 high income, 33 upper middle income, 37 lower middle income, and only 19 lower income countries in the data set. This is important to keep in mind when looking at barstacks that only include count instead of frequency (Attitude Towards Violence by Income Group).

A stacked bar helps in the visualization and reveals that indeed most of the countries that have a value of 0.25 on Laws on Domestic Violence (better laws on domestic violence) are high income countries. However, it is important to note that in the 0.75 value, there seems to be a more balanced distribution throughout all income groups. In the value of “1,” i.e., laws on domestic violence that completely discriminate against women, we find only upper middle income countries.

We also produced a bar stack for prevalence of violence in the lifetime, but instead of using a proportion stacked bar we opted for a frequency one. Even though this plot suffers from the total number of high income countries being greater than the number of countries in other income categories, the concentration of such countries in the left side of the graph once again reveal the pattern of high income countries performing better in such indicators.

After drawing analysis by Income Group, we make a comparison between all three indicators to determine their relationship to one another.

Intuitively, we would expect prevalence of violence and attitudes towards violence to have a positive linear relationship: As more women agree that their husbands are justified in beating them, domestic violence will become more widespread. We create a scatter plot to verify that our data follows this intuition.

The x-axis shows attitudes towards violence, the y-axis shows prevalence of violence in the lifetime, and the color indicates the level of laws on domestic violence. The visualization shows a positive linear relationship between prevalence of violence in the Lifetime and attitudes towards violence (correlation coefficient = 0.45).

Looking at laws on domestic violence, we see the the yellow countries (level equals 0.25) are concentrated in the bottom left side of the plot, thus revealing that countries that have better laws on Domestic Violence also tend to a lower prevalence of domestic violence and better attitudes towards women. However, the 0.75, 0.5 and 1 levels of laws do not appear to have a particular pattern and are spread throughout the graph.

The annotated countries in the graph are the two countries where the laws on domestic violence are completely discriminatory against women: Equatorial Guinea and the Russian Federation. Equatorial Guinea has a higher prevalance of violence and worse attitudes towards women, which agrees with our intuition. However, the Russian Federation is an interesting case because it has a relatively low prevalence of violence and better attitudes towards violence, but its legislation completely discriminates against women. It is closer to the yellow dots on the graph, which are countries with better laws on domestic violence. This sets Russia apart as a notable outlier when plotting the relationship between the three dependent variable. It would be interesting to conduct further research on Russia when analyzing potential causes for the persistence of violence against women.

Comparing OECD and UN in Prevalence of Violence Against Women

To determine whether the OECD indicator is an accurate representation of the prevalence of domestic violence against women, we decided to compare it to other available indicators on domestic violence. We have chosen the UN indicator, “Proportion of women subjected to physical and/or sexual violence by a current or former intimate partner in the last 12 months” (“Gender Statistics - Violence Against Women” n.d.).

There are some clear distinctions between the UN and OECD indicators. While they both measure the proportion of women who have experienced violence by an intimate partner, the UN indicator only includes women who have experienced violence in the past 12 months, while the OECD includes those who experienced violence at any point in their lives Therefore, we would expect the OECD indicator to be larger than the UN indicator for most countries, given that many more women would have experience violence at some point in their lives.

Another limitation is that the UN indicator comes from many different reference years. Some countries have collected this data in more recent years (2015), while others last collected this data in the year 2000. This discrepancy makes comparison between countries and between indicators more difficult, since peoples’ attitudes towards domestic violence and countries’ laws and regulations may have changed significantly over the years, potentially reducing the prevalence of domestic violence. In addition, all the data from the OECD indicator was collected in 2019, much more recently than most of the UN data. Nonetheless, it would be interesting to find out whether both indicators are consistent with each other.

The figure above shows two maps of the world, with colored markers to represent the prevalence of violence in different countries. The first map visualizes the OECD indicator, while the second map visualizes the UN indicator. As expected, we can see that the OECD map has much darker and larger markers in general than the UN map, suggesting that the prevalence of violence is higher in the OECD data. This makes sense since the OECD measured the prevalence of violence over womens’ lifetimes, while the UN only measured violence in the past 12 months.

The two indicators also appear to be consistent across different regions. For instance, in both maps, countries in the Global North appear to have less prevalence of domestic violence, as shown by the smaller, lighter markers. In contrast, markers across the Global South are consistently darker and larger, revealing patterns of higher prevalence of domestic violence.

We use a scatter plot to verify the relation we observed between the UN and OECD indicators. As we can see, the indicators appear to have a very strong positive relationship, with a correlation coefficient of 0.7561751. This comparison verifies the accuracy of the OECD data by comparing it to an external source.

Mapping Attitudes Towards Violence

Having observed the patterns of prevalence of violence across world regions, we now look at attitudes towards violence from the same perspective.

This map shows the attitudes towards violence against women across different countries. Similarly to the pattern we observed in prevalence of violence, the wealthier and more developed countries of the global North seem to have a lower share of women who justify violence than countries in the global South. This observation further reinforces our interest in explaining these indicators using measures of economic development and opportunity.

Issues with the Dependent Variable Data and Limitations

Before moving on with our analysis, it is important to note the limitations and issues with our dependent variables.

Categorizing countries by income group might reveal different patterns of violence against women across countries of different income levels, but it tells us little about the causes of these discrepancies. Hence, whereas our graphs were helpful in revealing possible paths we can take in our research, they did not necessarily reveal the causes of violence against women. In addition, using income group as a category might ignore nuances regaridng the socio-economic development of different countries. For example, Brazil is classified as an Upper Middle Income country, but it is also one of the most unequal countries in the world (Gini Coefficient = 53.3), ranking 9th globally. Such great inequality could be driving some of our dependent variables, and this would not be revealed by income group.

The OECD indicators themselves also have several limitations. There are some discrepancies between what the indicators are trying to measure and what they actually measure. Since violence comes in many diverse forms, it is difficult to perfectly define it or accurately measure it. For example, the prevalence in lifetime indicator only quantifies violence from an intimate partner, excluding levels of harassment that come from outside intimate circles, such as rape. Similarly, to measure “attitude towards violence,” the data set creators used “the percentage of women who agree that a husband/partner is justified in beating his wife/partner under certain circumstances.” Why is this particular question used as a proxy for attitude towards violence? Why are the men not asked whether they are justified in beating women? Are there better or more comprehensive questions to gauge attitude towards violence?

In addition, for the “laws on domestic violence” indicator, the dataset creators do not explain how they quantified the abstract concept of “laws and practices.” They also do not specify what they mean by laws that “fully discriminate against women’s rights.” This makes it difficult to determine the accuracy of the indicator.

The dataset also does not contain all the countries in the world. If we perform an anti-join of the countries dataset with the violence dataset, we can see that there are around 82 missing countries. Many of these countries are small islands that may not have enough data on violence against women.

Visualizing the Independent Variables

A Note on our Independent Variables Dataset

Our independent variables come from the Women’s Economic Opportunities Index, a composite index created by the Economist Intelligence Unit that evaluates whether a country’s environment is favorable towards female entrepreneurs and employees (“Women’s Economic Opportunity 2012” n.d.). The index was measured in two years: first in 2010, and later in 2012. We use the most recent 2012 dataset in our analysis. We downloaded the full dataset of this index as an Excel workbook from the Economist Intelligence Unit’s website. This original version can also be found in this repository under data/violence_factors. However, this Excel workbook contains several sheets and is not readable by dplyr’s csv importer, so we had to manually select the sheet we needed from Excel, clean it, and export it in a .csv format to import it into R Studio. In particular, we used the Excel sheet named Data2012, which contains the 2012 index score for every country, in addition to the breakdown of each score into its categories and sub-categories. Since the sheet was not in csv format and contained several merged columns and rows, we had to clean it up manually in Excel before importing it as a csv file into R Studio. The cleaned version of the sheet can be found in the file data/violence_factors/woe_data_cleaned.csv.

Here, we provide a brief exploration of our independent variables. We plot a histogram for the WEO Index as well as each of the four categories that fall under the index.

As the histograms show, all the categories as well as the indicator lie on a scale from 0 to 100, where 0 is least favourable for women and 100 is more favourable. We see that the education and training indicator and the legal status indicator are skewered more to the left of the scale, suggesting that most countries have favorable education and legal status for women. However, in the access to finance indicator, countries appear to be spread more uniformly across the scale. In terms of labour policy, most countries lie in the 50 units range. Finally, the WEO index itself appears to have a somewhat bimodal distribution with a peak around the 50 units range and another at the 75 units range.

Limitations of the Independent Variables

Our independent variables also have some limitations that may make it difficult to compare them with our dependent variables. Firstly, one important limitation is the difference in dates between our dependent and independent variables. Whereas the OECD violence against women data was measured in 2019, the WEOI was measured back in 2012. It might be the case that countries have significantly improved or worsen their scores in the WEOI in the time between 2012 and 2019, which could bias our analysis.

In addition, one major limiting factor is the fact that the variables we have explored in the Women’s Economic Index Composite index are composite variables. Hence, if we are looking for one specific variable that could explain violence against women, it would be unclear from the composite indicator which particular underlying indicator is the causal one. The large number of indicators (29) that contribute to the WEO index may give rise to many confounding variables that complicate our analysis. If all the independent indicators are correlated, there might be a confounding variable causing all of them and causing our dependent variables at the same time.

Similarly to our dependent variables, the WEO index cannot accurately or perfectly measure an abstract, complicated concept such as women’s economic opportunities. A lot of human biases and motivations are involved in the process of selecting which variables to combine into a single indicator. For example, since the indicator was developed by The Economist and sponsored by Exxon-Mobil, they mostly considered private sector entrepreneurship and initiatives as a measure of women’s economic opportunities, rather than taking into account public policy and state-sponsored opportunities. In addition, many of the qualitative indicators in the index, such as the availability and affordability of childcare, did not exist and had to be created and measured by analysts from the Economist Intelligence Unit (“Women’s Economic Opportunity 2012” n.d.). Therefore, these qualitative assessments were made by external agents rather than being made by national statistics agencies, possibly introducing external biases into the data and excluding local knowledge and data.

A final limitation is that our independent variable was only measured for 128 countries, providing us with less data to build our regression model on

Part 2: Predicting Attitude Towards Violence

Correlation Coefficients

Having explored out dependent and independent variables separately, we proceed to explore the relationship between them.

First, we want to get a general sense of the relationship between the women’s economic opportunity index and the three dependent variables. Is the WEOI correlated at all with our variables?

           [,1]
attitude  -0.68
law       -0.17
prev_viol -0.37

We can infer from the correlation coefficients that the WEOI is negatively correlated with all of the dependent variables to varying extents, as we would have expected. As women receive more economic opportunities, we would expect the prevalence of domestic violence to decrease, attitudes towards women to become more favourable, and laws on domestic violence to become more supportive of survivors.

We are intrigued by the fact that attitudes towards violence against women has the strongest correlation with the WEOI (correlation coefficient = -0.68). Therefore, we decided to proceed with this variable and further examine its relation with the categories that make up the WEOI.

We can see from this correlation matrix that attitudes towards violence against women has a moderate to strong negative correlation with all the WEOI categories, especially education and training. In addition, the WEOI indicators seem to be strongly correlated positively with each other. Intuitively, it makes sense for these correlations to exist as countries where women are better educated are usually those where women have better access to finance, a better legal status, and more favourable labour policies. However, these correlations complicate our effort to find an explanatory variable for violence against women, as it is difficult to disentangle which of these indicators is responsible for any effects we observe on attitudes towards violence.

Causal Diagram

To help us better understand the complicated relationships between the different variables, we draw a causal diagram.

This diagram showcases the relations between both the dependent and independent variables, incorporating external variables as well. We assumed that, among our dependent variables, prevalence of violence against women can be caused by discriminatory laws on domestic violence and supportive attitudes towards violence. Among our independent variables, we noted that the four categories (labour policy and practice, education and training, woman’s legal status and access to finance) are all direct causes of the WEOI, since the index is composed of them. In addition, one common cause we noted for all of these variables could be democracy, since democratic countries are likely to have political structures that are conducive to the economic empowerment of women. Democratic political structures in these countries allow women to raise issues that concern them and vote for better education, access to finance, labour policies, and legal status.

We now use our causal diagram to determine the set of variables we need to adjust for when predicting our dependent variables using our independent variables.

We only run the adjustment set method on one independent variable: legal status. However, the resulting adjustment set is analogous for the other independent variables. The result is two adjustment sets: democracy or the rest of the independent variables (finance, education, labour policy). We may need to adjust for democracy because it is a direct cause of all our independent variables. We may also need to adjust for the other independent variables since they can all potentially be causes of attitudes towards violence.

Regressions

Next, we run regression models to evaluate whether there is a linear relationship between attitudes towards violence and our independent variables. We first run univariate regressions between the attitudes indicator and each of the indicators to observe their individual effects before making adjustments. Then, we run multivariate regressions to adjust for confounding or causal variables.

After modelling a linear relationship between the women’s opportunity index and attitudes towards violence, we find that with evey one unit increase in the women’s oppurtunity index, there is a decrease of -0.33% in the share of women who agree that men are justified in beating their wives. This effect has a very low standard error (NA), which means there is less uncertainty about this relationship. As the plot shows, the coefficient of the WEOI has a 95% confidence interval of NA and NA.

Next, we model the relationship between attitudes towards violence and each of the four categories.

With every one unit increase in Labour Policy and Practice there is a decrease of -0.5 in attitudes towards violence. This variable also has a low standard error of NA, meaning we can be fairly certain that labour policy has a negative impact on the share of women who justify being abused. The 95% confidence interval for the coefficient ranges from NA to NA.

The estimate for Access to Finance is also -0.47. Hence, With every one unit increase in Access to Finance there is a decrease of -0.47 in attitudes towards violence. The confidence levels range are a bit smaller then from Labour Policy and Practice, ranging from -0.57 and -0.37, with an also smaller uncertainty of 0.05.

The estimate for Education and Training is -0.59. Hence, With every one unit increase in Education and Training there is a decrease of -0.59 in attitudes towards violence. This is a more significant change then the previous two variables explored.The confidence levels range from -0.69 and -0.49, with an uncertainty of 0.05.

The estimate for Women’s Legal Status is -0.80. This is the highest estimate we found. Hence, With every one unit increase in Women’s Legal Status there is a decrease of -0.80 in attitudes towards violence. The confidence levels range from -0.95 and -0.66, with an uncertainty of 0.07. This is the indicator with strongest correlation with a reduction in Attitude Towards Violence.

Also noticing that the confidence intervals of each of the regressions is fairly small which means the uncertainty interval is fairly small. Additionally, the standard error of each coefficient were low.

With those results in mind, we draw a multivariate regression:

When running a multivariate regression of all of the four indicators with attitude towards violence we found that the indicators education, legal status and access to finance still hold a negative decrease effect on attitudes towards violence with every one unit increase, however labour policy now has a positive increase in attitudes towards violence with every one unit increase which is interesting to see. The confidence intervals of each has increased as well as the standard error coefficient which means there is a larger uncertainty range.

Even when running the multivariate regression the best indicator remains still to be the legal status with the highest negitave decrease in attitudes towards violence with the coefficients of -0.45. Therefore, further exploration on the indicator should be done.

Breakdown of Legal Status

When running a regression on further exploration of the indicator legal status, we found that within the factors under legal status the one with the highest effect on attitudes towards violence was property ownership with a negative decrease of -0.19 with every on unit increase with a uncertainty of 0.06 and a confidence interval of -0.31 and -0.07.

As seen from the graph as well as from the regression we ran on the indicator legal status, a higher level on property ownership rights results in a decrease in positive attitudes towards violence. While it is possible for outliers to be impacting the data, the graph demonstrates a linear decrease pattern between the two.

We then continued by observing the relationship between contraception usage, which is a factor that falls under the legal status indicator, and attitudes towards violence. That’s because in our previous analysis contraception usage came second highest in negatively decreasing attitudes towards violence with a decrease of -0.18 with a confidence interval of -0.32 and -0.04 with an uncertainty of 0.07.

From the graph we see that there is a negative relationship between contraception use and attitudes towards violence. The linear decrease pattern is more observable in this graph as the data is fairly clustered around the line as well as a decrease motion is visible while there still is a few outliers.

Upon reflecting on what are some of the factors that might be influencing or causing women’s legal status, we moved our research forward by analyzing the role that democracy has on women’s legal status.

Exploring the Effects of Democracy

We ran three regressions one in which observed the relationship between democracy and legal status, another observed the relationship between democracy and attitudes towards violence and a final multivariate regression observing the relationship between legal status and democracy on attitudes towards violence.

For the first regression with every one unit increase in democracy there is a 6.06 positive increase in legal status with a confidence interval of 4.99 and 7.10 with an uncertainty of 0.54.

For the second regression with every one unit increase in democracy there is a negative decrease of -6.99 in attitudes towards violence with a confidence interval of -8.50 and -5.52 with an uncertainty of 0.76.

As for the multivariate regression of legal status and democracy on attitudes towards violence with every one unit increase in democracy there is a negative decrease of -1.53 on attitudes towards violence with a confidence interval of -3.80 and 0.69 and an uncertainty of 1.13. For every one unit increase in legal status there is a negative decrease in attitudes towards violence by -0.68 with a confidence interval of -0.96 and -0.40 with an uncertainty of 0.14.

Interestingly we can clearly see that democracy does in fact have an affect on legal status which could be a reason for why legal status indicator had the highest effect on attitudes towards violence.

Analysis and Interpretation of Results

When we ran the regression between attitudes towards violence individually with each of the four indicators from the WEOI, we noticed that women’s legal status seemed to be the most strongly negatively correlated with the dependent variable (-0.80). Running a multivariate regression with all indicators, however, decreased the correlation of women’s legal status to (-0.45). Even though it remains moderately correlated, such interaction reveal that it is difficult to isolate one single cause for the increase or decrease of levels in attitudes towards violence. Interestingly, when we ran the multivariate regression, the indicator Labour Policy and Practice went from a negative coefficient of -0.47 to 0.21.

A counterfactual that arises from our research is imagining a country in which the women’s legal status was significantly lower than the rest of the indicators. Would such a country have lower levels of attitude against violence (less women agreeing that violence is justified)? Even though we cannot find such a case within the indicators of the WOEI, the exploration on the dependent variables highlighted the Russian Federation as having laws on domestic violence that completely discriminated against women but had decent levels in prevalence of violence in lifetime and attitude towards women. A further study on the case of the Russian Federation could reveal the effectiveness of women’s legal status (and specifically laws against domestic violence) in causing a decrease in violence against women.

we found that among the sub-indicators that form Women’s Legal Status, property rights and contraceptive use were among the ones driving Attitude Towards Violence the most, with respectively coefficients of -0.19 (uncertainty of 0.06) and -0.18 (uncertainty of 0.07).

When analyzing the causal relationship between the democracy index and the legal status of women, we notice that democracy seems to be driving Women’s legal status up, which in turn drives attitude towards violence to decrease. This was an interesting finding: the higher the levels of democracy, the higher the status of women legally, which in turn produce lower levels of acceptance of violence against women.

Hence, our findings do corroborated that strengthening democracy and, hence, the legal status of women might be a path for decreasing attitude towards violence. Increasing women’s legal status can be also be done by significantly increasing women’s property rights and contraception use.

Obviously, it might be far-fetched to suggest democracy as a remedy for decreasing attitude towards violence across the globe, as it suggests this is applicable to all cultures. It is important to take into account different political and socio-economy, as well as historical contingencies, when designing public policy and in the process of policy-making.

Conclusions

We cannot single out or highlight one single indicator in the WEOI that is most influential in decreasing violence against women. Our research highlights the complexities and the varying number of factors that result in the persistence of violence against women. However, it still might be useful to investigate further the sub-indicators within each indicator for the WEOI and analyze what they can provide as insights. Singling out countries, like Russia, and analyze closely what is the situation in violence against women through both quantitative and qualitative research can further reveal indicators that can be useful in measuring degrees of such forms of gendered violence.

While the four indicators we observed and analysed did in fact demonstrate a decrease in attitudes towards violence there are confounding variables that we perhaps have not considered which makes it hard to come to a causal conclusion. It is also interesting to see that indicators when observed separately demonstrate an effect on attitudes towards violence however when observed alongside other indicators have completely opposing effects.

This studies shows how data is complex and immense while certain indicators and factors might demonstrate an effect on a variable the reason for such an effect could be due to many confounding variables and other reasoning to what one might original have thought.

With that being said, the “top-to-bottom” structure of this research, in which we started from one single index and proceeded to slowly peeling off its layers was extremely insightful in terms of shedding light into how complex indexes and composite variables are constructed. It highlights how several measures, some obvious, some more ominous, converse to each other to finally measure an index. It also reminds us that the construction of such indexes are attempts to measure incredibly abstract concepts into truly useful ones. Whereas this was helpful for us in schematizing causal diagrams, we also found ourselves constantly puzzled on what we were truly measuring.

References